1
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Caratti A, Squara S, Bicchi C, Tao Q, Geschwender D, Reichenbach SE, Ferrero F, Borreani G, Cordero C. Augmented visualization by computer vision and chromatographic fingerprinting on comprehensive two-dimensional gas chromatographic patterns: Unraveling diagnostic signatures in food volatilome. J Chromatogr A 2023; 1699:464010. [PMID: 37116300 DOI: 10.1016/j.chroma.2023.464010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/18/2023] [Accepted: 04/20/2023] [Indexed: 04/30/2023]
Abstract
Computer Vision is an approach of Artificial Intelligence (AI) that conceptually enables "computers and systems to derive useful information from digital images" giving access to higher-level information and "take actions or make recommendations based on that information". Comprehensive two-dimensional chromatography gives access to highly detailed, accurate, yet unstructured information on the sample's chemical composition, and makes it possible to exploit the AI concepts at the data processing level (e.g., by Computer Vision) to rationalize raw data explorations. The goal is the understanding of the biological phenomena interrelated to a specific/diagnostic chemical signature. This study introduces a novel workflow for Computer Vision based on pattern recognition algorithms (i.e., combined untargeted and targeted UT fingerprinting) which includes the generation of composite Class Images for representative samples' classes, their effective re-alignment and registration against a comprehensive feature template followed by Augmented Visualization by comparative visual analysis. As an illustrative application, a sample set originated from a Research Project on artisanal butter (from raw sweet cream to ripened butter) is explored, capturing the evolution of volatile components along the production chain and the impact of different microbial cultures on the finished product volatilome. The workflow has significant advantages compared to the classical one-step pairwise comparison process given the ability to realign and pairwise compare both targeted and untargeted chromatographic features belonging to Class Images resembling chemical patterns from many different samples with intrinsic biological variability.
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Affiliation(s)
- Andrea Caratti
- Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino, Via Pietro Giuria 9, Turin I-10125, Italy
| | - Simone Squara
- Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino, Via Pietro Giuria 9, Turin I-10125, Italy
| | - Carlo Bicchi
- Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino, Via Pietro Giuria 9, Turin I-10125, Italy
| | | | | | - Stephen E Reichenbach
- GC Image LLC, Lincoln, NE, USA; Computer Science and Engineering Department, University of Nebraska - Lincoln, Lincoln, NE, USA
| | - Francesco Ferrero
- Department of Agricultural, Forestry and Food Sciences, Università di Torino, Grugliasco TO, Italy
| | - Giorgio Borreani
- Department of Agricultural, Forestry and Food Sciences, Università di Torino, Grugliasco TO, Italy
| | - Chiara Cordero
- Dipartimento di Scienza e Tecnologia del Farmaco, Università di Torino, Via Pietro Giuria 9, Turin I-10125, Italy.
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2
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Squara S, Manig F, Henle T, Hellwig M, Caratti A, Bicchi C, Reichenbach SE, Tao Q, Collino M, Cordero C. Extending the breadth of saliva metabolome fingerprinting by smart template strategies and effective pattern realignment on comprehensive two-dimensional gas chromatographic data. Anal Bioanal Chem 2023; 415:2493-2509. [PMID: 36631574 PMCID: PMC10149478 DOI: 10.1007/s00216-023-04516-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/16/2022] [Accepted: 01/03/2023] [Indexed: 01/13/2023]
Abstract
Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consistency may be affected by random systemic fluctuations and/or changes in the experimental parameters. This study details the optimization of a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years. The strategy was trained on two batches: one with samples from healthy subjects who had undergone dietary intervention with high/low-Maillard reaction products (dataset A), and the second from healthy/unhealthy obese individuals (dataset B). The combined untargeted and targeted pattern recognition algorithm (i.e., UT fingerprinting) was tuned for key process parameters, the signal-to-noise ratio (S/N), and MS spectrum similarity thresholds, and then tested for the best transform function (global or local, affine or low-degree polynomial) for pattern realignment in the temporal domain. Reliable peak detection achieved its best performance, computed as % of false negative/positive matches, with a S/N threshold of 50 and spectral similarity direct match factor (DMF) of 700. Cross-alignment of bi-dimensional (2D) peaks in the temporal domain was fully effective with a supervised operation including multiple centroids (reference peaks) and a match-and-transform strategy using affine functions. Regarding the performance-derived response fluctuations, the most promising strategy for cross-comparative analysis and data fusion included the mass spectral total useful signal (MSTUS) approach followed by Z-score normalization on the resulting matrix.
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Affiliation(s)
- Simone Squara
- Dipartimento Di Scienza E Tecnologia del Farmaco, Università Degli Studi Di Torino, Via Pietro Giuria 9, 10125, Turin, Italy
| | - Friederike Manig
- Food Chemistry, Technische Universität Dresden, Dresden, Germany
| | - Thomas Henle
- Food Chemistry, Technische Universität Dresden, Dresden, Germany
| | - Michael Hellwig
- Special Food Chemistry, Technische Universität Dresden, Dresden, Germany
| | - Andrea Caratti
- Dipartimento Di Scienza E Tecnologia del Farmaco, Università Degli Studi Di Torino, Via Pietro Giuria 9, 10125, Turin, Italy
| | - Carlo Bicchi
- Dipartimento Di Scienza E Tecnologia del Farmaco, Università Degli Studi Di Torino, Via Pietro Giuria 9, 10125, Turin, Italy
| | - Stephen E Reichenbach
- Computer Science and Engineering Department, University of Nebraska, Lincoln, NE, USA.,GC Image LLC, Lincoln, NE, USA
| | | | - Massimo Collino
- Dipartimento Di Neuroscienze "Rita Levi Montalcini", University of Turin, Turin, Italy.
| | - Chiara Cordero
- Dipartimento Di Scienza E Tecnologia del Farmaco, Università Degli Studi Di Torino, Via Pietro Giuria 9, 10125, Turin, Italy.
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3
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Stilo F, Bicchi C, Jimenez-Carvelo AM, Cuadros-Rodriguez L, Reichenbach SE, Cordero C. Chromatographic fingerprinting by comprehensive two-dimensional chromatography: Fundamentals and tools. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2020.116133] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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4
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Wilde MJ, Zhao B, Cordell RL, Ibrahim W, Singapuri A, Greening NJ, Brightling CE, Siddiqui S, Monks PS, Free RC. Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software. Anal Chem 2020; 92:13953-13960. [PMID: 32985172 PMCID: PMC7644112 DOI: 10.1021/acs.analchem.0c02844] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Comprehensive
two-dimensional gas chromatography (GC×GC) is
a powerful analytical tool for both nontargeted and targeted analyses.
However, there is a need for more integrated workflows for processing
and managing the resultant high-complexity datasets. End-to-end workflows
for processing GC×GC data are challenging and often require multiple
tools or software to process a single dataset. We describe a new approach,
which uses an existing underutilized interface within commercial software
to integrate free and open-source/external scripts and tools, tailoring
the workflow to the needs of the individual researcher within a single
software environment. To demonstrate the concept, the interface was
successfully used to complete a first-pass alignment on a large-scale
GC×GC metabolomics dataset. The analysis was performed by interfacing
bespoke and published external algorithms within a commercial software
environment to automatically correct the variation in retention times
captured by a routine reference standard. Variation in 1tR and 2tR was reduced on average
from 8 and 16% CV prealignment to less than 1 and 2% post alignment,
respectively. The interface enables automation and creation of new
functions and increases the interconnectivity between chemometric
tools, providing a window for integrating data-processing software
with larger informatics-based data management platforms.
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Affiliation(s)
- Michael J Wilde
- School of Chemistry, University of Leicester, University Road, Leicester LE1 7RH, U.K.,Department of Respiratory Sciences, University of Leicester, University Road, Leicester LE1 7RH, U.K
| | - Bo Zhao
- Leicester NIHR Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, U.K
| | - Rebecca L Cordell
- School of Chemistry, University of Leicester, University Road, Leicester LE1 7RH, U.K
| | - Wadah Ibrahim
- Department of Respiratory Sciences, University of Leicester, University Road, Leicester LE1 7RH, U.K.,Leicester NIHR Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, U.K
| | - Amisha Singapuri
- Department of Respiratory Sciences, University of Leicester, University Road, Leicester LE1 7RH, U.K.,Leicester NIHR Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, U.K
| | - Neil J Greening
- Department of Respiratory Sciences, University of Leicester, University Road, Leicester LE1 7RH, U.K.,Leicester NIHR Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, U.K
| | - Chris E Brightling
- Department of Respiratory Sciences, University of Leicester, University Road, Leicester LE1 7RH, U.K.,Leicester NIHR Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, U.K
| | - Salman Siddiqui
- Department of Respiratory Sciences, University of Leicester, University Road, Leicester LE1 7RH, U.K.,Leicester NIHR Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, U.K
| | - Paul S Monks
- School of Chemistry, University of Leicester, University Road, Leicester LE1 7RH, U.K
| | - Robert C Free
- Leicester NIHR Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, U.K
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5
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Stilo F, Gabetti E, Bicchi C, Carretta A, Peroni D, Reichenbach SE, Cordero C, McCurry J. A step forward in the equivalence between thermal and differential-flow modulated comprehensive two-dimensional gas chromatography methods. J Chromatogr A 2020; 1627:461396. [DOI: 10.1016/j.chroma.2020.461396] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/06/2020] [Accepted: 07/07/2020] [Indexed: 12/18/2022]
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6
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Li Z, Kim S, Zhong S, Zhong Z, Kato I, Zhang X. Coherent Point Drift Peak Alignment Algorithms Using Distance and Similarity Measures for Two-Dimensional Gas Chromatography Mass Spectrometry Data. JOURNAL OF CHEMOMETRICS 2020; 34:e3236. [PMID: 33505107 PMCID: PMC7837599 DOI: 10.1002/cem.3236] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 03/18/2020] [Indexed: 06/12/2023]
Abstract
The peak alignment is a vital preprocessing step before downstream analysis, such as biomarker discovery and pathway analysis, for two-dimensional gas chromatography mass spectrometry (2DGCMS)-based metabolomics data. Due to uncontrollable experimental conditions, e.g., the differences in temperature or pressure, matrix effects on samples, and stationary phase degradation, a shift of retention times among samples inevitably occurs during 2DGCMS experiments, making it difficult to align peaks. Various peak alignment algorithms have been developed to correct retention time shifts for homogeneous, heterogeneous or both type of mass spectrometry data. However, almost all existing algorithms have been focused on a local alignment and are suffering from low accuracy especially when aligning dense biological data with many peaks. We have developed four global peak alignment (GPA) algorithms using coherent point drift (CPD) point matching algorithms: retention time-based CPD-GPA (RT), prior CPD-GPA (P), mixture CPD-GPA (M), and prior mixture CPD-GPA (P+M). The method RT performs the peak alignment based only on the retention time distance, while the methods P, M, and P+M carry out the peak alignment using both the retention time distance and mass spectral similarity. The method P incorporates the mass spectral similarity through prior information and the methods M and P+M use the mixture distance measure. Four developed algorithms are applied to homogeneous and heterogeneous spiked-in data as well as two real biological data and compared with three existing algorithms, mSPA, SWPA, and BiPACE-2D. The results show that our CPD-GPA algorithms perform better than all existing algorithms in terms of F1 score.
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Affiliation(s)
- Zeyu Li
- Department of Computer Sciences, Wayne State University, Detroit, MI 48202
| | - Seongho Kim
- Biostatistics Core, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201
- Department of Oncology, School of Medicine, Wayne State University, Detroit, MI 48201
| | - Sikai Zhong
- Department of Computer Sciences, Wayne State University, Detroit, MI 48202
| | - Zichun Zhong
- Department of Computer Sciences, Wayne State University, Detroit, MI 48202
| | - Ikuko Kato
- Department of Oncology, School of Medicine, Wayne State University, Detroit, MI 48201
- Department of Pathology, Wayne State University, Detroit, MI 48201
| | - Xiang Zhang
- Department of Chemistry, University of Louisville, Louisville, KY 40209
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7
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Rosso MC, Mazzucotelli M, Bicchi C, Charron M, Manini F, Menta R, Fontana M, Reichenbach SE, Cordero C. Adding extra-dimensions to hazelnuts primary metabolome fingerprinting by comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry featuring tandem ionization: Insights on the aroma potential. J Chromatogr A 2019; 1614:460739. [PMID: 31796248 DOI: 10.1016/j.chroma.2019.460739] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 11/22/2019] [Accepted: 11/25/2019] [Indexed: 12/20/2022]
Abstract
The information potential of comprehensive two-dimensional gas chromatography combined with time of flight mass spectrometry (GC × GC-TOFMS) featuring tandem hard (70 eV) and soft (12 eV) electron ionization is here applied to accurately delineate high-quality hazelnuts (Corylus avellana L.) primary metabolome fingerprints. The information provided by tandem signals for untargeted and targeted 2D-peaks is examined and exploited with pattern recognition based on template matching algorithms. EI-MS fragmentation pattern similarity, base-peak m/z values at the two examined energies (i.e., 12 and 70 eV) and response relative sensitivity are adopted to evaluate the complementary nature of signals. As challenging bench test, the hazelnut primary metabolome has a large chemical dimensionality that includes various chemical classes such as mono- and disaccharides, amino acids, low-molecular weight acids, and amines, further complicated by oximation/silylation to obtain volatile derivatives. Tandem ionization provides notable benefits including larger relative ratio of structural informing ions due to limited fragmentation at low energies (12 eV), meaningful spectral dissimilarity between 12 and 70 eV (direct match factor values range 222-783) and, for several analytes, enhanced relative sensitivity at lower energies. The complementary information provided by tandem ionization is exploited by untargeted/targeted (UT) fingerprinting on samples from different cultivars and geographical origins. The responses of 138 UT-peak-regions are explored to delineate informative patterns by univariate and multivariate statistics, providing insights on correlations between known precursors and (key)-aroma compounds and potent odorants. Strong positive correlations between non-volatile precursors and odorants are highlighted with some interesting linear trends for: 3-methylbutanal with isoleucine (R2 0.9284); 2,3-butanedione/2,3-pentanedione with monosaccharides (fructose/glucose derivatives) (R2 0.8543 and 0.8860); 2,5-dimethylpyrazine with alanine (R2 0.8822); and pyrroles (1H-pyrrole, 3-methyl-1H-pyrrole, and 1H-pyrrole-2-carboxaldehyde) with ornithine and alanine derivatives (R2 0.8604). The analytical work-flow provides a solid foundation for a new strategy for hazelnuts quality assessment because aroma potential could be derived from precursors' chemical fingerprints.
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Affiliation(s)
- Marta Cialiè Rosso
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, I-10125 Torino 6707172, Italy
| | - Maria Mazzucotelli
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, I-10125 Torino 6707172, Italy
| | - Carlo Bicchi
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, I-10125 Torino 6707172, Italy
| | | | | | - Roberto Menta
- Soremartec Italia Srl, Ferrero Group, Alba (CN), Italy
| | - Mauro Fontana
- Soremartec Italia Srl, Ferrero Group, Alba (CN), Italy
| | - Stephen E Reichenbach
- Computer Science and Engineering Department, University of Nebraska - Lincoln, NE, USA; GC Image LCC, Lincoln, NE, USA
| | - Chiara Cordero
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, Via Pietro Giuria 9, I-10125 Torino 6707172, Italy.
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8
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Morimoto J, Rosso MC, Kfoury N, Bicchi C, Cordero C, Robbat A. Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome. Molecules 2019; 24:E3757. [PMID: 31635337 PMCID: PMC6832143 DOI: 10.3390/molecules24203757] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 09/04/2019] [Accepted: 10/15/2019] [Indexed: 01/26/2023] Open
Abstract
Identifying all analytes in a natural product is a daunting challenge, even if fractionated by volatility. In this study, comprehensive two-dimensional gas chromatography/mass spectrometry (GC×GC-MS) was used to investigate relative distribution of volatiles in green, pu-erh tea from leaves collected at two different elevations (1162 m and 1651 m). A total of 317 high and 280 low elevation compounds were detected, many of them known to have sensory and health beneficial properties. The samples were evaluated by two different software. The first, GC Image, used feature-based detection algorithms to identify spectral patterns and peak-regions, leading to tentative identification of 107 compounds. The software produced a composite map illustrating differences in the samples. The second, Ion Analytics, employed spectral deconvolution algorithms to detect target compounds, then subtracted their spectra from the total ion current chromatogram to reveal untargeted compounds. Compound identities were more easily assigned, since chromatogram complexities were reduced. Of the 317 compounds, for example, 34% were positively identified and 42% were tentatively identified, leaving 24% as unknowns. This study demonstrated the targeted/untargeted approach taken simplifies the analysis time for large data sets, leading to a better understanding of the chemistry behind biological phenomena.
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Affiliation(s)
- Joshua Morimoto
- Department of Chemistry, Tufts University, Medford, MA 02155, USA.
| | - Marta Cialiè Rosso
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, 10125 Turin, Italy.
| | - Nicole Kfoury
- Department of Chemistry, Tufts University, Medford, MA 02155, USA.
| | - Carlo Bicchi
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, 10125 Turin, Italy.
| | - Chiara Cordero
- Dipartimento di Scienza e Tecnologia del Farmaco, Università degli Studi di Torino, 10125 Turin, Italy.
| | - Albert Robbat
- Department of Chemistry, Tufts University, Medford, MA 02155, USA.
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9
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Highly Informative Fingerprinting of Extra-Virgin Olive Oil Volatiles: The Role of High Concentration-Capacity Sampling in Combination with Comprehensive Two-Dimensional Gas Chromatography. SEPARATIONS 2019. [DOI: 10.3390/separations6030034] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The study explores the complex volatile fraction of extra-virgin olive oil by combining high concentration-capacity headspace approaches with comprehensive two-dimensional gas chromatography, which is coupled with time of flight mass spectrometry. The static headspace techniques in this study are: (a) Solid-phase microextraction, with multi-polymer coating (SPME- Divinylbenzene/Carboxen/Polydimethylsiloxane), which is taken as the reference technique; (b) headspace sorptive extraction (HSSE) with either a single-material coating (polydimethylsiloxane—PDMS) or a dual-phase coating that combines PDMS/Carbopack and PDMS/EG (ethyleneglycol); (c) monolithic material sorptive extraction (MMSE), using octa-decyl silica combined with graphite carbon (ODS/CB); and dynamic headspace (d) with either PDMS foam, operating in partition mode, or Tenax TA™, operating in adsorption mode. The coverage of both targeted and untargeted 2D-peak-region features, which corresponds to detectable analytes, was examined, while concentration factors (CF) for a selection of informative analytes, including key-odorants and off-odors, and homolog-series relative ratios were calculated and the information capacity was discussed. The results highlighted the differences in concentration capacities, which were mainly caused by polymer-accumulation characteristics (sorptive/adsorptive materials) and its amount. The relative concentration capacity for homologues and potent odorants was also discussed, while headspace linearity and the relative distribution of analytes, as a function of different sampling amounts, was examined. This last point is of particular interest in quantitative studies where accurate data is needed to derive consistent conclusions.
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10
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Reichenbach SE, Zini CA, Nicolli KP, Welke JE, Cordero C, Tao Q. Benchmarking machine learning methods for comprehensive chemical fingerprinting and pattern recognition. J Chromatogr A 2019; 1595:158-167. [DOI: 10.1016/j.chroma.2019.02.027] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 02/04/2019] [Accepted: 02/11/2019] [Indexed: 11/29/2022]
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11
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Stilo F, Liberto E, Reichenbach SE, Tao Q, Bicchi C, Cordero C. Untargeted and Targeted Fingerprinting of Extra Virgin Olive Oil Volatiles by Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry: Challenges in Long-Term Studies. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2019; 67:5289-5302. [PMID: 30994349 DOI: 10.1021/acs.jafc.9b01661] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Comprehensive two-dimensional gas chromatography coupled with mass spectrometric detection (GC × GC-MS) offers an information-rich basis for effective chemical fingerprinting of food. However, GC × GC-MS yields 2D-peak patterns (i.e., sample 2D fingerprints) whose consistency may be affected by variables related to either the analytical platform or to the experimental parameters adopted for the analysis. This study focuses on the complex volatile fraction of extra-virgin olive oil and addresses 2D-peak patterns variations, including MS signal fluctuations, as they may occur in long-term studies where pedo-climatic, harvest year, or shelf life changes are studied. The 2D-pattern misalignments are forced by changing chromatographic settings and MS acquisition. All procedural steps, preceding pattern recognition by template matching, are analyzed and a rational workflow defined to accurately realign patterns and analytes metadata. Signal-to-noise ratio (SNR) detection threshold, reference spectra extraction, and similarity match factor threshold are critical to avoid false-negative matches. Distance thresholds and polynomial transform parameters are key for effective template matching. In targeted analysis (supervised workflow) with optimized parameters, method accuracy reaches 92.5% (i.e., % of true-positive matches) while for combined untargeted and targeted ( UT) fingerprinting (unsupervised workflow), accuracy reaches 97.9%. Response normalization also is examined, evidencing good performance of multiple internal standard normalization that effectively compensates for discriminations occurring during injection of highly volatile compounds. The resulting workflow is simple, effective, and time efficient.
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Affiliation(s)
- Federico Stilo
- Dipartimento di Scienza e Tecnologia del Farmaco , Università degli Studi di Torino , Turin I-10125 , Italy
| | - Erica Liberto
- Dipartimento di Scienza e Tecnologia del Farmaco , Università degli Studi di Torino , Turin I-10125 , Italy
| | - Stephen E Reichenbach
- Computer Science and Engineering Department , University of Nebraska , Lincoln , Nebraska 68588 , United States
- GC Image, LLC , Lincoln , Nebraska 68508 , United States
| | - Qingping Tao
- GC Image, LLC , Lincoln , Nebraska 68508 , United States
| | - Carlo Bicchi
- Dipartimento di Scienza e Tecnologia del Farmaco , Università degli Studi di Torino , Turin I-10125 , Italy
| | - Chiara Cordero
- Dipartimento di Scienza e Tecnologia del Farmaco , Università degli Studi di Torino , Turin I-10125 , Italy
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12
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Cordero C, Kiefl J, Reichenbach SE, Bicchi C. Characterization of odorant patterns by comprehensive two-dimensional gas chromatography: A challenge in omic studies. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2018.06.005] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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13
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Cordero C, Schmarr HG, Reichenbach SE, Bicchi C. Current Developments in Analyzing Food Volatiles by Multidimensional Gas Chromatographic Techniques. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2018; 66:2226-2236. [PMID: 28110527 DOI: 10.1021/acs.jafc.6b04997] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper presents current developments and future perspectives on the spread of advanced analytical tasks in the field of food volatile analysis. The topics outlined comprise (a) recent advances on miniaturized sampling techniques; (b) the potential and challenges of multidimensional gas chromatography coupled with mass spectrometric detection for volatile identification and quantitation in samples with complex matrices;
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Affiliation(s)
- Chiara Cordero
- Dipartimento di Scienza e Tecnologia del Farmaco , Università di Torino , Turin , Italy
| | - Hans-Georg Schmarr
- Dienstleistungszentrum Ländlicher Raum (DLR) - Rheinpfalz , Institut für Weinbau und Oenologie , Breitenweg 71 , D-67435 Neustadt an der Weinstraße , Germany
- Faculty of Chemistry, Instrumental Analytical Chemistry , University Duisburg-Essen , Universitätsstraße 5 , 45141 Essen , Germany
| | | | - Carlo Bicchi
- Dipartimento di Scienza e Tecnologia del Farmaco , Università di Torino , Turin , Italy
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14
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Magagna F, Liberto E, Reichenbach SE, Tao Q, Carretta A, Cobelli L, Giardina M, Bicchi C, Cordero C. Advanced fingerprinting of high-quality cocoa: Challenges in transferring methods from thermal to differential-flow modulated comprehensive two dimensional gas chromatography. J Chromatogr A 2018; 1536:122-136. [DOI: 10.1016/j.chroma.2017.07.014] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 05/02/2017] [Accepted: 07/04/2017] [Indexed: 11/30/2022]
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15
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Prebihalo SE, Berrier KL, Freye CE, Bahaghighat HD, Moore NR, Pinkerton DK, Synovec RE. Multidimensional Gas Chromatography: Advances in Instrumentation, Chemometrics, and Applications. Anal Chem 2017; 90:505-532. [DOI: 10.1021/acs.analchem.7b04226] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sarah E. Prebihalo
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - Kelsey L. Berrier
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - Chris E. Freye
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - H. Daniel Bahaghighat
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
- Department of Chemistry and Life Science, United States Military Academy, West Point, New York 10996, United States
| | - Nicholas R. Moore
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - David K. Pinkerton
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
| | - Robert E. Synovec
- Department of Chemistry, University of Washington, Box 351700, Seattle, Washington 98195, United States
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Pixel-by-pixel correction of retention time shifts in chromatograms from comprehensive two-dimensional gas chromatography coupled to high resolution time-of-flight mass spectrometry. J Chromatogr A 2017. [DOI: 10.1016/j.chroma.2017.05.065] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Cordero C, Rubiolo P, Reichenbach SE, Carretta A, Cobelli L, Giardina M, Bicchi C. Method translation and full metadata transfer from thermal to differential flow modulated comprehensive two dimensional gas chromatography: Profiling of suspected fragrance allergens. J Chromatogr A 2017; 1480:70-82. [DOI: 10.1016/j.chroma.2016.12.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 11/19/2016] [Accepted: 12/07/2016] [Indexed: 02/03/2023]
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